Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.
Maps are a fundamental component of an autonomous vehicles system. An autonomous driving vehicle (ADV) relies on various terrestrial maps to make decisions and perform various driving functions autonomously in real-time. For example, localization map may provide localization data for an ADV to determine a precise location of the ADV based on 2D or 3D scans of an environment surrounding the ADV. A road graph may provide road boundary, traffic light positions. A static map may provide static object information near the ADV to offload computational burden of the ADV.
Existing approaches to generate localization maps may be described as “top-down synchronization.” In these approaches, dedicated map data collection cars, mounted with various sensors, such as, a global positioning satellite detector (GPS), inertial measurement unit sensor (IMU), radio detection and ranging (RADAR), and light detection and ranging (LIDAR) are driven on roadways to capture image data and corresponding coarse and/or fine poses of the cars. A pose may be described as a position (x, y, z) and orientation (azimuth, pitch, roll). Afterwards, these captured image data are image processed offline. The captured image data are processed with techniques, such as iterative closest point (ICP) or simultaneous localization and mapping (SLAM), which may or may not involve global optimization, to link together the captured image data into a unified localization map. Thereafter road graphs and static maps are generated from the unified localization map. After map generation, maps are synced to customers' autonomous driving vehicle in a top down fashion, hence the “top down synchronization” approach.
The disadvantages of the “top-down synchronization” map generation approach include inflexibilities in adapting to dynamic elements such as growing tress, accumulation of snow, and parked cars on the road when the image data is captured but subsequently moved; a high requirement on sensors and sensors calibration; and a high cost associated with maintaining dedicated data collection cars.